generated from LufsX/Experiment-LogReg
116 lines
3.2 KiB
Python
116 lines
3.2 KiB
Python
# function.py
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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"""
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实验说明:
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- 自行编写实现数据加载与预测病马死亡的对率回归算法。
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注意事项:
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- 在 function.py 中实现对率回归算法。
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- 可以自行导入其他库，需要在 requirements.txt 添加依赖，若未在 requirements.txt 中添加使用到的依赖，则无法自动评测，成绩为零。
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- 自动评测脚本仅会加载 train_logreg、predict_logreg 和 load_data 函数，请勿修改函数名。
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测评环境:
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- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
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- OS: Ubuntu 22.04.4 LTS
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- Python: 3.10.12
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- numpy: 2.1.1
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"""
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"""
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请在这里填写学号和姓名，不填写即成绩无效。
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"""
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id = "202210423130"
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name = "董秋"
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# 这里写自己的代码实现
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def train_logreg(X, y, learning_rate=0.01, iterations=1000,lambda_reg=0.01):
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"""
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对率回归训练函数。
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Args:
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- X (numpy array): 特征数据。
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- y (numpy array): 标签数据。
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- other_params (str): 其它参数，可选设置。若需设置其它参数，必须设置其默认值，否则将**无法通过**自动评测，**成绩为零*。
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Returns:
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- numpy array: 训练得到的权重。
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"""
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m, n = X.shape
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weights = np.zeros(n)
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for _ in range(iterations):
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z = np.dot(X, weights)
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predictions = 1 / (1 + np.exp(-z))
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gradient = np.dot(X.T, (predictions - y)) / m
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# L2正则化
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gradient += (lambda_reg / m) * weights
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weights -= learning_rate * gradient
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return weights
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def predict_logreg(inX, weights):
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"""
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对率回归预测函数。
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Args:
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- inX (numpy array): 输入待分类的样本。
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- weights (numpy array): 训练得到的权重。
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Returns:
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- int: 预测的分类标签。
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"""
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z = np.dot(inX, weights)
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prediction_prob = 1 / (1 + np.exp(-z))
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return 1 if prediction_prob >= 0.5 else 0
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# 这里也写自己的代码实现
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def load_data(filepath):
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data = []
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labels = []
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with open(filepath, 'r') as f:
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for line in f:
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values = line.strip().replace('?', '0').split()
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try:
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values = [float(v) for v in values]
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data.append(values[:-1]) # 特征
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labels.append(values[-1]) # 标签
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except ValueError:
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continue
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return np.array(data), np.array(labels)
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def preprocess_data(X):
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# 特征标准化
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scaler = StandardScaler()
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return scaler.fit_transform(X)
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if __name__ == "__main__":
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# 以下代码可用于测试，可自行随意修改
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# 加载训练集
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X_train, y_train = load_data("./data/horse-colic.data")
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X_train = preprocess_data(X_train)
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# 加载测试集
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X_test, y_test = load_data("./data/horse-colic.test")
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X_test = preprocess_data(X_test)
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# 训练对率回归模型
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weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000,lambda_reg=0.01)
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X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, random_state=42)
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# 测试单个数据
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y_pred = predict_logreg(X_test[11], weights)
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print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
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